class_docstr: Hellinger Distance split criterion.
  
  The Hellinger distance is a measure of distributional divergence.
  It is used as the splitting criterion [^1] on decision trees to to address
  the imbalanced data problem.
  
  References
  ----------
  [^1]: Cieslak, David A., T. Ryan Hoens, Nitesh V. Chawla, and W. Philip Kegelmeyer.
  "Hellinger distance decision trees are robust and skew-insensitive." Data Mining
  and Knowledge Discovery 24, no. 1 (2012): 136-158.
code: def __init__(self, min_branch_fraction):
      super().__init__()
      self.min_branch_fraction = min_branch_fraction
  
file_path: river/river/tree/split_criterion/hellinger_distance_criterion.py
imports: from __future__ import annotations
  import math
  from .base import SplitCriterion
label_desc: Use image node_id 1 to create a new HellingerDistanceCriterion object from inherited base classes: SplitCriterion with example: obj = HellingerDistanceCriterion(min_branch_fraction)